The conditional phrase translation probabilities constitute the principal components of phrase-based machine translation systems.
These probabilities are estimated using a heuristic method that does not seem to optimize any reasonable objective function
of the word-aligned, parallel training corpus. Earlier efforts on devising a better understood estimator either do not scale
to reasonably sized training data, or lead to deteriorating performance. In this paper we explore a new approach based on
three ingredients (1) A generative model with a prior over latent segmentations derived from Inversion Transduction Grammar
(ITG), (2) A phrase table containing all phrase pairs without length limit, and (3) Smoothing as learning objective using
a novel Maximum-A-Posteriori version of Deleted Estimation working with Expectation-Maximization. Where others conclude that
latent segmentations lead to overfitting and deteriorating performance, we show here that these three ingredients give performance
equivalent to the heuristic method on reasonably sized training data.

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